Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Indeterminate Structure01:18

Indeterminate Structure

Indeterminate structures refer to structures where internal forces and reactions cannot be determined using only the equations of static equilibrium.  Indeterminate structures have more unknown forces and reaction forces than equations of static equilibrium that can be used to determine them. Indeterminate structures are often used in engineering to create complex, efficient, and aesthetically pleasing structures. There are various types of indeterminate structures used in engineering and some...
Deformation of Member under Multiple Loadings01:11

Deformation of Member under Multiple Loadings

When a rod is made of different materials or has various cross-sections, it must be divided into parts that meet the necessary conditions for determining the deformation. These parts are each characterized by their internal force, cross-sectional area, length, and modulus of elasticity. These parameters are then used to compute the deformation of the entire rod.
In the case of a member with a variable cross-section, the strain is not constant but depends on the position. The deformation of an...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Explainable Machine Learning for Early Detection of Mild Cognitive Impairment, Fall Risk, and Frailty Using Sensor-Based Motor Function Data.

medRxiv : the preprint server for health sciences·2026
Same author

A machine learning approach to concussive group classification using discrete outcome measures from a low-cost movement-based assessment system.

Medical engineering & physics·2025
Same author

Accelerometer measurement error in a randomized physical activity intervention trial in breast cancer survivors was nondifferential but attenuated the intervention effect.

The international journal of behavioral nutrition and physical activity·2025
Same author

Countermovement Jump Performance Is Altered by Visual and Auditory Cognitive Dual Tasking in Recreationally Active Young Adults: A Cross-Sectional Study.

Journal of sport rehabilitation·2025
Same author

Coordination and variability of muscular activation in male athletes with and without subacromial impingement syndrome: A case-control study.

PloS one·2025
Same author

Feasibility of Using a Novel, Multimodal Motor Function Assessment Platform With Machine Learning to Identify Individuals With Mild Cognitive Impairment.

Alzheimer disease and associated disorders·2024
Same journal

Retraction.

The open biomedical engineering journal·2023
Same journal

Retraction Notice: Research and Implementation of Children's Speech Signal Processing System.

The open biomedical engineering journal·2019
Same journal

Retraction Notice: Research on Algorithm of Extracting PPG Signal for Detecting Atrial Fibrillation based on Probability Density Function.

The open biomedical engineering journal·2019
Same journal

Research and Implementation of Children's Speech Signal Processing System.

The open biomedical engineering journal·2019
Same journal

Research on Algorithm of Extracting PPG Signal for Detecting Atrial Fibrillation based on Probability Density Function.

The open biomedical engineering journal·2019
Same journal

F.E.M. Stress-Investigation of Scolios Apex.

The open biomedical engineering journal·2018
See all related articles

Related Experiment Video

Updated: May 23, 2026

A Non-Invasive Method for Generating the Cyclic Loading-Induced Intra-Articular Cartilage Lesion Model of the Rat Knee
05:04

A Non-Invasive Method for Generating the Cyclic Loading-Induced Intra-Articular Cartilage Lesion Model of the Rat Knee

Published on: July 5, 2021

Computational knee ligament modeling using experimentally determined zero-load lengths.

Katherine H Bloemker1, Trent M Guess, Lorin Maletsky

  • 1Musculoskeletal Biomechanics Research Lab, Department of Civil and Mechanical Engineering, University of Missouri - Kansas City, Kansas City, MO.

The Open Biomedical Engineering Journal
|April 24, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a subject-specific method for determining knee ligament zero-load lengths in computational models. Accurate zero-load lengths are crucial for precise knee joint simulations and predicting joint motion.

Keywords:
Computational knee modelingligament parametersreference strainzero-load length.

More Related Videos

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

Athymic Rat Model for Evaluation of Engineered Anterior Cruciate Ligament Grafts
10:32

Athymic Rat Model for Evaluation of Engineered Anterior Cruciate Ligament Grafts

Published on: March 26, 2015

Related Experiment Videos

Last Updated: May 23, 2026

A Non-Invasive Method for Generating the Cyclic Loading-Induced Intra-Articular Cartilage Lesion Model of the Rat Knee
05:04

A Non-Invasive Method for Generating the Cyclic Loading-Induced Intra-Articular Cartilage Lesion Model of the Rat Knee

Published on: July 5, 2021

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

Athymic Rat Model for Evaluation of Engineered Anterior Cruciate Ligament Grafts
10:32

Athymic Rat Model for Evaluation of Engineered Anterior Cruciate Ligament Grafts

Published on: March 26, 2015

Area of Science:

  • Biomechanics
  • Computational modeling
  • Orthopedics

Background:

  • Accurate computational knee models are essential for understanding joint mechanics.
  • Ligament properties, particularly zero-load length, significantly influence knee joint kinematics.
  • Subject-specific modeling requires precise input parameters for biological structures.

Purpose of the Study:

  • To develop and validate a subject-specific method for determining the zero-load lengths of knee cruciate and collateral ligaments.
  • To assess the sensitivity of computational knee model kinematics to variations in ligament zero-load lengths.
  • To establish optimal methods for defining ligament zero-load lengths applicable across subjects.

Main Methods:

  • Utilized three cadaver knees tested in a dynamic knee simulator.
  • Performed manual envelope of motion testing to determine passive range of motion and zero-load lengths.
  • Created computational multibody knee models with 1D non-linear spring damper elements for ligaments.
  • Compared model kinematics to experimental kinematics during a simulated walk cycle.

Main Results:

  • Knee kinematics demonstrated high sensitivity to changes in ligament zero-load length.
  • The study identified optimal methods for defining ligament bundle zero-load lengths.
  • Manual envelope of motion measurements effectively determined the passive range of motion for zero-load length calculation.

Conclusions:

  • Zero-load length is a critical parameter in accurate knee joint modeling.
  • The proposed method for determining zero-load lengths is effective for subject-specific computational models (in vitro and in vivo).
  • Manual envelope of motion testing is a reliable approach for obtaining passive knee range of motion data.